Social recommender systems (SocialRS) simultaneously leverage the user-to-item
interactions as well as the user-to-user social relations for the task of generating item …

Exploring user-item interaction cues is crucial for the performance of recommender systems.
Explicit investigation of interaction cues is made possible by using graph-based models …

Abstract Cross-Domain Recommendation (CDR) has been proved helpful in dealing with
two bottlenecks in recommendation scenarios: data sparsity and cold start. Recent research …

Graph Neural Networks (GNNs) have recently been utilized to build Collaborative Filtering
(CF) models to predict user preferences based on historical user-item interactions. However …

Contrastive learning (CL) has recently catalyzed a productive avenue of research for
recommendation. The efficacy of most CL methods for recommendation may hinge on their …

Social recommendation based on social network has achieved great success in improving
the performance of the recommendation system. Since social network (user-user relations) …

Encoding only the task-related information from the raw data,\ie, disentangled
representation learning, can greatly contribute to the robustness and generalizability of …

As a new recommendation pattern combining collaborative filtering and social network,
social recommender system strives to introduce auxiliary user relations to alleviate data …

Modeling the dynamics of sequential patterns (ie, sequential recommendation) has obtained
great attention, where the key problem is how to infer the next interesting item according to …

In an era where social media platforms burgeon with diverse content, compelling
moderation is imperative to filter harmful materials. Traditional methods often grapple with …